Machine Learning MCQ : Test 8

Explore this diverse selection of multiple-choice questions (MCQs) designed for various examinations. Machine Learning MCQ : Test 8 focuses on essential aspects of the subject, ensuring comprehensive preparation across different categories and fields of study to enhance your knowledge and readiness. The right answers for each question is provided next to respective questions for your convenience, you can either attend the test or dirtectly access the right answers by clicking the show correct answer button

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1. What is the purpose of using a Gaussian mixture model (GMM)?

A. To reduce data dimensions

B. To model data as a mixture of Gaussian distributions

C. To cluster data points

D. To increase model complexity

2. How does the BERT model improve NLP tasks?

A. By clustering data points

B. By pre-training on a large corpus of text

C. By reducing data dimensions

D. By increasing model complexity

3. Which method is used for dimensionality reduction in sparse data?

A. Principal component analysis (PCA)

B. Singular value decomposition (SVD)

C. Linear regression

D. Decision tree

4. What is the role of an optimizer in neural networks?

A. To reduce data dimensions

B. To minimize the loss function

C. To cluster data points

D. To increase model complexity

5. Which algorithm is used for time series forecasting?

A. K-means

B. ARIMA

C. Decision tree

D. Logistic regression

6. What is the purpose of using a dropout layer in neural networks?

A. To increase the data size

B. To prevent overfitting

C. To reduce dimensionality

D. To perform clustering

7. Which algorithm is commonly used for feature selection in high-dimensional data?

A. Principal Component Analysis (PCA)

B. Recursive Feature Elimination (RFE)

C. K-means clustering

D. Linear regression

8. How does an autoencoder learn to compress data?

A. By reducing data size

B. By reconstructing the input data

C. By clustering data points

D. By increasing model complexity

9. What is the purpose of early stopping in training neural networks?

A. To increase model complexity

B. To prevent overfitting by stopping training early

C. To reduce data size

D. To perform clustering

10. Which method is used to evaluate the performance of a multi-class classification model?

A. Confusion matrix

B. ROC curve

C. Precision-recall curve

D. Mean squared error

11. How does an ensemble method like stacking work?

A. By using a single model

B. By combining predictions of multiple models

C. By reducing data dimensions

D. By clustering data points

12. Which algorithm is commonly used for detecting anomalies in time series data?

A. K-means

B. Decision tree

C. Isolation forest

D. ARIMA

13. What is the primary advantage of using an LSTM network?

A. High computational efficiency

B. Ability to capture long-term dependencies

C. Simple implementation

D. Reduced model complexity

14. Which method is used to handle missing data in a dataset?

A. Data imputation

B. Feature scaling

C. Dimensionality reduction

D. Clustering

15. What is the purpose of a learning rate schedule in training neural networks?

A. To reduce data size

B. To dynamically adjust the learning rate

C. To perform clustering

D. To increase model complexity

16. Which evaluation metric is used for imbalanced classification problems?

A. Accuracy

B. Precision-Recall AUC

C. Mean absolute error

D. Silhouette score

17. How does a reinforcement learning agent learn from the environment?

A. By clustering data points

B. By receiving rewards and penalties

C. By reducing data dimensions

D. By increasing model complexity

18. What is the main advantage of using a Transformer model in NLP?

A. High computational efficiency

B. Ability to handle long-range dependencies

C. Simple implementation

D. Reduced model complexity

19. Which technique is used to handle high cardinality categorical features?

A. One-hot encoding

B. Embedding layers

C. Principal component analysis

D. Decision tree

20. How does the gradient clipping technique help in training RNNs?

A. By reducing data dimensions

B. By preventing exploding gradients

C. By clustering data points

D. By increasing model complexity

21. What is the main advantage of using a capsule network?

A. High computational efficiency

B. Ability to capture spatial hierarchies

C. Simple implementation

D. Reduced model complexity

22. Which algorithm is used for outlier detection?

A. K-means clustering

B. Isolation forest

C. Logistic regression

D. Decision tree

23. How does an attention mechanism improve sequence modeling?

A. By clustering data points

B. By focusing on relevant parts of the input sequence

C. By reducing data dimensions

D. By increasing model complexity

24. Which method is used for dimensionality reduction in high-dimensional data?

A. Linear regression

B. t-Distributed Stochastic Neighbor Embedding (t-SNE)

C. Decision tree

D. K-means clustering

25. What is the main purpose of using a variational autoencoder (VAE)?

A. To reduce data dimensions

B. To generate new data samples

C. To cluster data points

D. To increase model complexity

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